C. Journal articles published externallyhttp://hdl.handle.net/10986/4401
Fri, 09 Dec 2016 15:35:50 GMT2016-12-09T15:35:50ZDeliberate Disengagementhttp://hdl.handle.net/10986/25398
Deliberate Disengagement
Croke, Kevin; Grossman, Guy; Larreguy, Horacio A.; Marshall, John
A large literature examining advanced and consolidating democracies suggests that education increases political participation. However, in electoral authoritarian regimes, educated voters may instead deliberately disengage. If education increases critical capacities, political awareness, and support for democracy, educated citizens may believe that participation is futile or legitimizes autocrats. We test this argument in Zimbabwe—a paradigmatic electoral authoritarian regime—by exploiting cross-cohort variation in access to education following a major educational reform. We find that education decreases political participation, substantially reducing the likelihood that better-educated citizens vote, contact politicians, or attend community meetings. Consistent with deliberate disengagement, education’s negative effect on participation dissipated following 2008’s more competitive election, which (temporarily) initiated unprecedented power sharing. Supporting the mechanisms underpinning our hypothesis, educated citizens experience better economic outcomes, are more interested in politics, and are more supportive of democracy, but are also more likely to criticize the government and support opposition parties.
Mon, 01 Aug 2016 00:00:00 GMThttp://hdl.handle.net/10986/253982016-08-01T00:00:00ZHow Much of the Labor in African Agriculture Is Provided by Women?http://hdl.handle.net/10986/25373
How Much of the Labor in African Agriculture Is Provided by Women?
Palacios-Lopez, Amparo; Christiaensen, Luc; Kilic, Talip
The contribution of women to labor in African agriculture is regularly quoted in the range of 60–80%. Using individual, plot-level labor input data from nationally representative household surveys across six Sub-Saharan African countries, this study estimates the average female labor share in crop production at 40%. It is slightly above 50% in Malawi, Tanzania, and Uganda, and substantially lower in Nigeria (37%), Ethiopia (29%), and Niger (24%). There are no systematic differences across crops and activities, but female labor shares tend to be higher in households where women own a larger share of the land and when they are more educated. Controlling for the gender and knowledge profile of the respondents does not meaningfully change the predicted female labor shares. The findings question prevailing assertions regarding substantial gains in aggregate crop output as a result of increasing female agricultural productivity.
Sat, 15 Oct 2016 00:00:00 GMThttp://hdl.handle.net/10986/253732016-10-15T00:00:00ZEvaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areashttp://hdl.handle.net/10986/25372
Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas
Aubrecht, Christoph; León Torres, José Antonio
This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale.
Thu, 04 Feb 2016 00:00:00 GMThttp://hdl.handle.net/10986/253722016-02-04T00:00:00ZConsistent Yet Adaptive Global Geospatial Identification of Urban–Rural Patternshttp://hdl.handle.net/10986/25370
Consistent Yet Adaptive Global Geospatial Identification of Urban–Rural Patterns
Aubrecht, Christoph; Gunasekera, Rashmin; Ungar, Joachim; Ishizawa, Oscar
The main motivation of this paper is to shed new light on the problem of spatial identification of urban and rural areas globally, and to provide a compatible disaggregation framework for linking associated country-specific, non-spatial data compilations, such as building type inventories. Existing homogeneously set-up global urban extent models commonly ignore local-level specifics. While global consistency and regional comparability of urban characteristics are much strived-for goals in the global development and remote sensing communities, non-conformity at the national level often renders such models inapplicable for effective decision-making. Furthermore, the focus on identifying ‘urban’ leads to an ill-defined ‘rural’, which is simply defined by contrast as ‘everything else’; a questionable definition when referring to strongly spatially localized residential patterns. In this paper we introduce the novel iURBAN geospatial modeling approach, identifying Urban–Rural patterns in Built-up-Adjusted and Nationally-adaptive manner. The model operates at global scale, but at the same time conforms to country specifics. In this model, high-resolution, satellite-derived, built-up data is used to consistently detect global human settlements at unprecedented spatial detail. In combination with global gridded population data, and with reference to national level statistical information on urban population ratios globally compiled in the annually-released UN World Urbanization Prospects, iURBAN identifies matching urban extents. Additionally, a novel reallocation algorithm is introduced which addresses the poor representation of rural areas that is inherent in existing global population grids. Associating all of the population with inhabitable, built-up area and conforming to national urban–rural ratios, iURBAN sets a new standard by enabling careful consideration of both urban and rural as opposed to traditional urban-biased approaches.
Thu, 01 Dec 2016 00:00:00 GMThttp://hdl.handle.net/10986/253702016-12-01T00:00:00Z